File size: 2,638 Bytes
29a6c53 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | import os
import time
import joblib
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.metrics import classification_report, confusion_matrix
def labels_to_numeric(labels_df):
# 0 - BE, 1 - CA, 2 - CH, 3 - FR
labels_df["Country"] = labels_df["Country"].replace({'BE': 0})
labels_df["Country"] = labels_df["Country"].replace({'CA': 1})
labels_df["Country"] = labels_df["Country"].replace({'CH': 2})
labels_df["Country"] = labels_df["Country"].replace({'FR': 3})
print(np.array(labels_df.values).flatten())
return list(np.array(labels_df.values).flatten())
def load_data(data_dir, feats_fname, labels_fname, scope):
# Paths
feats_path = os.path.join(data_dir, feats_fname)
labels_path = os.path.join(data_dir, labels_fname)
# Load features
features = np.loadtxt(feats_path, delimiter=',')
print(scope, " features shape: ", features.shape)
# Load labels
labels_df = pd.read_csv(labels_path)
labels = labels_to_numeric(labels_df)
print(scope, " labels length: ", len(labels))
return features, labels
def fine_tune_xgb(X_train, y_train, model_fname):
# Initialize the classifier
clf = xgb.XGBClassifier(
max_depth=200,
n_estimators=400,
subsamples=1,
learning_rate=0.07,
reg_lambda=0.1,
reg_alpha=0.1,
gamma=1)
start = time.time()
clf.fit(X_train, y_train)
end = time.time()
print("======> Elapsed time for training with one set of parameters: %.10f" % (end - start))
# Save model
joblib.dump(clf, model_fname)
return clf
if __name__ == "__main__":
# Data directory
data_dir = "../data/bert_embeddings/"
# Load the data
train_features, train_labels = load_data(data_dir, "train_embeddings.csv", "train_labels.txt", "Train")
val_features, val_labels = load_data(data_dir, "val_embeddings.csv", "val_labels.txt", "Validation")
test_features, test_labels = load_data(data_dir, "test_embeddings.csv", "test_labels.txt", "Test")
# Fine tune
clf = fine_tune_xgb(train_features, train_labels, "xgb_model.joblib")
# Test
test_preds = clf.predict(test_features)
print("Test results:")
print(confusion_matrix(test_labels, test_preds))
print(classification_report(test_labels, test_preds, digits=6, target_names=["BE", "CA", "CH" ,"FR"]))
# Validation
val_preds = clf.predict(val_features)
print("Validation results:")
print(confusion_matrix(val_labels, val_preds))
print(classification_report(val_labels, val_preds, digits=6, target_names=["BE", "CA", "CH" ,"FR"]))
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